Class: PartialDependenceDisplay
Partial Dependence Plot (PDP).
This can also display individual partial dependencies which are often referred to as: Individual Condition Expectation (ICE).
It is recommended to use from_estimator to create a PartialDependenceDisplay. All parameters are stored as attributes.
Read more in Advanced Plotting With Partial Dependence and the User Guide.
Constructors
new PartialDependenceDisplay()
new PartialDependenceDisplay(
opts?):PartialDependenceDisplay
Parameters
| Parameter | Type | Description |
|---|---|---|
opts? | object | - |
opts.deciles? | any | Deciles for feature indices in features. |
opts.feature_names? | any | Feature names corresponding to the indices in features. |
opts.features? | any | Indices of features for a given plot. A tuple of one integer will plot a partial dependence curve of one feature. A tuple of two integers will plot a two-way partial dependence curve as a contour plot. |
opts.is_categorical? | any | Whether each target feature in features is categorical or not. The list should be same size as features. If undefined, all features are assumed to be continuous. |
opts.kind? | "average" | "individual" | "both" | Whether to plot the partial dependence averaged across all the samples in the dataset or one line per sample or both. |
opts.pd_results? | any | Results of partial_dependence for features. |
opts.random_state? | number | Controls the randomness of the selected samples when subsamples is not undefined. See Glossary for details. |
opts.subsample? | number | Sampling for ICE curves when kind is ‘individual’ or ‘both’. If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to be used to plot ICE curves. If int, represents the maximum absolute number of samples to use. Note that the full dataset is still used to calculate partial dependence when kind='both'. |
opts.target_idx? | number | In a multiclass setting, specifies the class for which the PDPs should be computed. Note that for binary classification, the positive class (index 1) is always used. |
Returns PartialDependenceDisplay
Defined in generated/inspection/PartialDependenceDisplay.ts:27
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/inspection/PartialDependenceDisplay.ts:25 |
_isInitialized | boolean | false | generated/inspection/PartialDependenceDisplay.ts:24 |
_py | PythonBridge | undefined | generated/inspection/PartialDependenceDisplay.ts:23 |
id | string | undefined | generated/inspection/PartialDependenceDisplay.ts:20 |
opts | any | undefined | generated/inspection/PartialDependenceDisplay.ts:21 |
Accessors
axes_
Get Signature
get axes_():
Promise<any>
If ax is an axes or undefined, axes_\[i, j\] is the axes on the i-th row and j-th column. If ax is a list of axes, axes_\[i\] is the i-th item in ax. Elements that are undefined correspond to a nonexisting axes in that position.
Returns Promise<any>
Defined in generated/inspection/PartialDependenceDisplay.ts:436
bars_
Get Signature
get bars_():
Promise<any>
If ax is an axes or undefined, bars_\[i, j\] is the partial dependence bar plot on the i-th row and j-th column (for a categorical feature). If ax is a list of axes, bars_\[i\] is the partial dependence bar plot corresponding to the i-th item in ax. Elements that are undefined correspond to a nonexisting axes or an axes that does not include a bar plot.
Returns Promise<any>
Defined in generated/inspection/PartialDependenceDisplay.ts:571
bounding_ax_
Get Signature
get bounding_ax_():
Promise<any>
If ax is an axes or undefined, the bounding_ax_ is the axes where the grid of partial dependence plots are drawn. If ax is a list of axes or a numpy array of axes, bounding_ax_ is undefined.
Returns Promise<any>
Defined in generated/inspection/PartialDependenceDisplay.ts:409
contours_
Get Signature
get contours_():
Promise<any>
If ax is an axes or undefined, contours_\[i, j\] is the partial dependence plot on the i-th row and j-th column. If ax is a list of axes, contours_\[i\] is the partial dependence plot corresponding to the i-th item in ax. Elements that are undefined correspond to a nonexisting axes or an axes that does not include a contour plot.
Returns Promise<any>
Defined in generated/inspection/PartialDependenceDisplay.ts:544
deciles_hlines_
Get Signature
get deciles_hlines_():
Promise<any>
If ax is an axes or undefined, vlines_\[i, j\] is the line collection representing the y axis deciles of the i-th row and j-th column. If ax is a list of axes, vlines_\[i\] corresponds to the i-th item in ax. Elements that are undefined correspond to a nonexisting axes or an axes that does not include a 2-way plot.
Returns Promise<any>
Defined in generated/inspection/PartialDependenceDisplay.ts:517
deciles_vlines_
Get Signature
get deciles_vlines_():
Promise<any>
If ax is an axes or undefined, vlines_\[i, j\] is the line collection representing the x axis deciles of the i-th row and j-th column. If ax is a list of axes, vlines_\[i\] corresponds to the i-th item in ax. Elements that are undefined correspond to a nonexisting axes or an axes that does not include a PDP plot.
Returns Promise<any>
Defined in generated/inspection/PartialDependenceDisplay.ts:490
figure_
Get Signature
get figure_():
Promise<any>
Figure containing partial dependence plots.
Returns Promise<any>
Defined in generated/inspection/PartialDependenceDisplay.ts:625
heatmaps_
Get Signature
get heatmaps_():
Promise<any>
If ax is an axes or undefined, heatmaps_\[i, j\] is the partial dependence heatmap on the i-th row and j-th column (for a pair of categorical features) . If ax is a list of axes, heatmaps_\[i\] is the partial dependence heatmap corresponding to the i-th item in ax. Elements that are undefined correspond to a nonexisting axes or an axes that does not include a heatmap.
Returns Promise<any>
Defined in generated/inspection/PartialDependenceDisplay.ts:598
lines_
Get Signature
get lines_():
Promise<any>
If ax is an axes or undefined, lines_\[i, j\] is the partial dependence curve on the i-th row and j-th column. If ax is a list of axes, lines_\[i\] is the partial dependence curve corresponding to the i-th item in ax. Elements that are undefined correspond to a nonexisting axes or an axes that does not include a line plot.
Returns Promise<any>
Defined in generated/inspection/PartialDependenceDisplay.ts:463
py
Get Signature
get py():
PythonBridge
Returns PythonBridge
Set Signature
set py(
pythonBridge):void
Parameters
| Parameter | Type |
|---|---|
pythonBridge | PythonBridge |
Returns void
Defined in generated/inspection/PartialDependenceDisplay.ts:83
Methods
dispose()
dispose():
Promise<void>
Disposes of the underlying Python resources.
Once dispose() is called, the instance is no longer usable.
Returns Promise<void>
Defined in generated/inspection/PartialDependenceDisplay.ts:139
from_estimator()
from_estimator(
opts):Promise<any>
Partial dependence (PD) and individual conditional expectation (ICE) plots.
Partial dependence plots, individual conditional expectation plots or an overlay of both of them can be plotted by setting the kind parameter. The len(features) plots are arranged in a grid with n_cols columns. Two-way partial dependence plots are plotted as contour plots. The deciles of the feature values will be shown with tick marks on the x-axes for one-way plots, and on both axes for two-way plots.
Read more in the User Guide.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.ax? | any | If a single axis is passed in, it is treated as a bounding axes and a grid of partial dependence plots will be drawn within these bounds. The n_cols parameter controls the number of columns in the grid. |
opts.categorical_features? | number | ArrayLike | Indicates the categorical features. |
opts.centered? | boolean | If true, the ICE and PD lines will start at the origin of the y-axis. By default, no centering is done. |
opts.contour_kw? | any | Dict with keywords passed to the matplotlib.pyplot.contourf call. For two-way partial dependence plots. |
opts.estimator? | any | A fitted estimator object implementing predict, predict_proba, or decision_function. Multioutput-multiclass classifiers are not supported. |
opts.feature_names? | ArrayLike | Name of each feature; feature_names\[i\] holds the name of the feature with index i. By default, the name of the feature corresponds to their numerical index for NumPy array and their column name for pandas dataframe. |
opts.features? | string | The target features for which to create the PDPs. If features\[i\] is an integer or a string, a one-way PDP is created; if features\[i\] is a tuple, a two-way PDP is created (only supported with kind='average'). Each tuple must be of size 2. If any entry is a string, then it must be in feature_names. |
opts.grid_resolution? | number | The number of equally spaced points on the axes of the plots, for each target feature. |
opts.ice_lines_kw? | any | Dictionary with keywords passed to the matplotlib.pyplot.plot call. For ICE lines in the one-way partial dependence plots. The key value pairs defined in ice_lines_kw takes priority over line_kw. |
opts.kind? | "average" | "individual" | "both" | Whether to plot the partial dependence averaged across all the samples in the dataset or one line per sample or both. |
opts.line_kw? | any | Dict with keywords passed to the matplotlib.pyplot.plot call. For one-way partial dependence plots. It can be used to define common properties for both ice_lines_kw and pdp_line_kw. |
opts.method? | string | The method used to calculate the averaged predictions: |
opts.n_cols? | number | The maximum number of columns in the grid plot. Only active when ax is a single axis or undefined. |
opts.n_jobs? | number | The number of CPUs to use to compute the partial dependences. Computation is parallelized over features specified by the features parameter. undefined means 1 unless in a joblib.parallel_backend context. \-1 means using all processors. See Glossary for more details. |
opts.pd_line_kw? | any | Dictionary with keywords passed to the matplotlib.pyplot.plot call. For partial dependence in one-way partial dependence plots. The key value pairs defined in pd_line_kw takes priority over line_kw. |
opts.percentiles? | any | The lower and upper percentile used to create the extreme values for the PDP axes. Must be in [0, 1]. |
opts.random_state? | number | Controls the randomness of the selected samples when subsamples is not undefined and kind is either 'both' or 'individual'. See Glossary for details. |
opts.response_method? | "auto" | "predict_proba" | "decision_function" | Specifies whether to use predict_proba or decision_function as the target response. For regressors this parameter is ignored and the response is always the output of predict. By default, predict_proba is tried first and we revert to decision_function if it doesn’t exist. If method is 'recursion', the response is always the output of decision_function. |
opts.sample_weight? | ArrayLike | Sample weights are used to calculate weighted means when averaging the model output. If undefined, then samples are equally weighted. If sample_weight is not undefined, then method will be set to 'brute'. Note that sample_weight is ignored for kind='individual'. |
opts.subsample? | number | Sampling for ICE curves when kind is ‘individual’ or ‘both’. If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to be used to plot ICE curves. If int, represents the absolute number samples to use. Note that the full dataset is still used to calculate averaged partial dependence when kind='both'. |
opts.target? | number | In a multiclass setting, specifies the class for which the PDPs should be computed. Note that for binary classification, the positive class (index 1) is always used. |
opts.verbose? | number | Verbose output during PD computations. |
opts.X? | ArrayLike[] | X is used to generate a grid of values for the target features (where the partial dependence will be evaluated), and also to generate values for the complement features when the method is 'brute'. |
Returns Promise<any>
Defined in generated/inspection/PartialDependenceDisplay.ts:160
init()
init(
py):Promise<void>
Initializes the underlying Python resources.
This instance is not usable until the Promise returned by init() resolves.
Parameters
| Parameter | Type |
|---|---|
py | PythonBridge |
Returns Promise<void>
Defined in generated/inspection/PartialDependenceDisplay.ts:96
plot()
plot(
opts):Promise<any>
Plot partial dependence plots.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.ax? | any | and a grid of partial dependence plots will be drawn within these bounds. The n_cols parameter controls the number of columns in the grid. |
opts.bar_kw? | any | Dict with keywords passed to the matplotlib.pyplot.bar call for one-way categorical partial dependence plots. |
opts.centered? | boolean | If true, the ICE and PD lines will start at the origin of the y-axis. By default, no centering is done. |
opts.contour_kw? | any | Dict with keywords passed to the matplotlib.pyplot.contourf call for two-way partial dependence plots. |
opts.heatmap_kw? | any | Dict with keywords passed to the matplotlib.pyplot.imshow call for two-way categorical partial dependence plots. |
opts.ice_lines_kw? | any | Dictionary with keywords passed to the matplotlib.pyplot.plot call. For ICE lines in the one-way partial dependence plots. The key value pairs defined in ice_lines_kw takes priority over line_kw. |
opts.line_kw? | any | Dict with keywords passed to the matplotlib.pyplot.plot call. For one-way partial dependence plots. |
opts.n_cols? | number | The maximum number of columns in the grid plot. Only active when ax is a single axes or undefined. |
opts.pd_line_kw? | any | Dictionary with keywords passed to the matplotlib.pyplot.plot call. For partial dependence in one-way partial dependence plots. The key value pairs defined in pd_line_kw takes priority over line_kw. |
opts.pdp_lim? | any | Global min and max average predictions, such that all plots will have the same scale and y limits. pdp_lim\[1\] is the global min and max for single partial dependence curves. pdp_lim\[2\] is the global min and max for two-way partial dependence curves. If undefined (default), the limit will be inferred from the global minimum and maximum of all predictions. |
Returns Promise<any>
Defined in generated/inspection/PartialDependenceDisplay.ts:326